A continually growing field, there are many practical applications for artificial intelligence in today’s business economy. From personalized advertising and product recommendations to fraud detection and revenue prediction, businesses have tons of data at their disposal which can provide extremely valuable insights for making better business decisions.Read Full Story
Deep-Data-Mining is pleased to introduce the new book, Principles of Database Management – The Practical Guide to Storing, Managing and Analyzing Big and Small Data, by Lemahieu W., vanden Broucke S., and Baesens B. (ISBN: 9781107186125). The following is the book interview.See http://admin.cambridge.Read Full Story
This blog post is the first in a series discussing different theoretical and practical aspects of the Frank-Wolfe algorithm.
In practical machine learning and data science tasks, an ML model is often used to quantify a global, semantically meaningful relationship between two or more values. For example, a hotel chain might want to use ML to optimize their pricing strategy and use a model to estimate the likelihood of a room being booked at a given price and day of the week.Read Full Story
We are excited to share a free extract of Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019: Evaluating a Classification Model with a Spam Filter.
This section reflects an important design decision in the book: teach model evaluation first, and as a step separate from model construction.
It is funny, but it takes some effort to teach in this way.
The first is Sunday afternon during the Industry Expo day. This one is meant to be quite practical, starting with an overview of Contextual Bandits and leading into how to apply the new Personalizer service, the first service in the world functionally supporting general contextual bandit learning.
The second is Friday morning. This one is more academic with many topics.
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning.In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley.Read Full Story
(Note: Technical post about practical methods to figure MLB distribution of player talent and regression to the mean.)——For a long time, we’ve been using the “Palmer/Tango” method to estimating the spread of talent among MLB teams. You’re probably sick of seeing it, but I’ll run it again real quick for 2013:1.Read Full Story